% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/logit_c.r
\name{logit_c}
\alias{logit_c}
\title{Conditional logit likelihood}
\usage{
logit_c(starts3, dat, otherdat, alts)
}
\arguments{
\item{starts3}{Starting values as a vector (num). For this likelihood,
the order takes: c([alternative-specific parameters],
[travel-distance parameters]). \cr \cr
The alternative-specific parameters and travel-distance parameters
are of length (# of alternative-specific variables) and (# of
travel-distance variables) respectively.}

\item{dat}{Data matrix, see output from shift_sort_x, alternatives with
distance.}

\item{otherdat}{Other data used in model (as a list containing objects
`intdat` and `griddat`). \cr \cr
For this likelihood, `intdat` are "travel-distance variables", which
are alternative-invariant variables that are interacted with travel
distance to form the cost portion of the likelihood. Each variable
name therefore corresponds to data with dimensions (number of
observations) by (unity), and returns a single parameter. \cr \cr
In `griddat` are "alternative-specific variables", that vary across
alternatives, e.g. catch rates. Each variable name therefore
corresponds to data with dimensions (number of observations) by
(number of alternatives), and returns a single parameter for each
variable (e.g. the marginal utility from catch). \cr \cr
For both objects any number of variables are allowed, as a list of
matrices. Note the variables (each as a matrix) within `griddat` and
`intdat` have no naming restrictions. "Alternative-specific
variables" may correspond to catches that vary by location, and
"travel-distance variables" may be vessel characteristics that affect
how much disutility is suffered by traveling a greater distance. Note
in this likelihood "alternative-specific variables" vary across
alternatives because each variable may have been estimated in a
previous procedure (i.e. a construction of expected catch). \cr \cr
If there are no other data, the user can set `griddat` as ones with
dimension (number of observations) by (number of alternatives) and
`intdat` variables as ones with dimension (number of observations) by
(unity).}

\item{alts}{Number of alternative choices in model as length equal to
unity (as a numeric vector).}
}
\value{
ld: negative log likelihood
}
\description{
Conditional logit likelihood
}
\section{Graphical examples}{
 
\if{html}{
\figure{logit_c_grid.png}{options: width="40\%" 
alt="Figure: logit_c_grid.png"}
\cr
\figure{logit_c_travel.png}{options: width="40\%" 
alt="Figure: logit_c_travel.png"}
}
}

\examples{
data(zi)
data(catch)
data(choice)
data(distance)
data(si)

optimOpt <- c(1000,1.00000000000000e-08,1,0)

methodname <- 'BFGS'

kk <- 4

si2 <- matrix(sample(1:5,dim(si)[1]*kk,replace=TRUE),dim(si)[1],kk)
zi2 <- sample(1:10,dim(zi)[1],replace=TRUE)

otherdat <- list(griddat=list(predicted_catch=as.matrix(predicted_catch),
    si2=as.matrix(si2)), intdat=list(zi=as.matrix(zi),
    zi2=as.matrix(zi2)))

initparams <- c(2.5, 2, -1, -2)

func <- logit_c

results <- discretefish_subroutine(catch,choice,distance,otherdat,
    initparams,optimOpt,func,methodname)

}
